GIScience and Remote Sensing

Aims & Scope

GIScience & Remote Sensing publishes original, peer-reviewed articles associated with geographic information systems (GIS), remote sensing of the environment (including digital image processing), geocomputation, spatial data mining, and geographic environmental modelling.

Papers reflecting both basic and applied research are published.

Metrics & Ranking

Abstracting & Indexing

Journal is indexed in leading academic databases, ensuring global visibility and accessibility of our peer-reviewed research.

• Scopus
• Google Scholar
• Directory of Open Access Journals (DOAJ)
• Web of Science

Subjects & Keywords

Journal’s research areas, covering key disciplines and specialized sub-topics in Earth and Planetary Sciences, designed to support cutting-edge academic discovery.

Licensing & Copyright

This journal operates under an Open Access model. Articles are freely accessible to the public immediately upon publication. The content is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0), allowing users to share and adapt the work with proper attribution.

Copyright remains with the author(s), and no permission is required for non-commercial use, provided the original source is cited.

Policy Links

This section provides access to essential policy documents, guidelines, and resources related to the journal’s publication and submission processes.

• Aims scope
• Homepage
• Oa statement
• Author instructions
• License terms
• Review url
• Board url
• Copyright url
• Plagiarism url
• Preservation url
• Apc url
• License

Plagiarism Policy

This journal follows a plagiarism policy. All submitted manuscripts are screened using reliable plagiarism detection software to ensure originality and academic integrity. Authors are responsible for proper citation and acknowledgment of all sources, and any form of plagiarism, including self-plagiarism, will not be tolerated.

For more details, please refer to our official: Plagiarism Policy.

APC Details

The journal’s Article Processing Charge (APC) policies support open access publishing in Earth and Planetary Sciences, ensuring accessibility and quality in research dissemination.

This journal requires an Article Processing Charge (APC) to support open access publishing, covering peer review, editing, and distribution. The current APC is 4,165.00 AUD. Learn more.

Explore journals without APCs for alternative publishing options.

Most Cited Articles

The Most Cited Articles section features the journal's most impactful research, based on citation counts. These articles have been referenced frequently by other researchers, indicating their significant contribution to their respective fields.

• An optimal parameters-based geographical detector model enhances geographic characteristics of explanatory variables for spatial heterogeneity analysis: cases with different types of spatial data

  Citation: 891

  Authors: Yongze, Jinfeng, Yong, Chengdong

• Land cover and land use classification performance of machine learning algorithms in a boreal landscape using Sentinel-2 data

  Citation: 464

  Authors: Abdulhakim Mohamed

• The Vegetation Drought Response Index (VegDRI): A New Integrated Approach for Monitoring Drought Stress in Vegetation

  Citation: 390

  Authors: Jesslyn F., Brian D., Tsegaye, Michael J., Bradley C.

• Remote sensing for wetland classification: a comprehensive review

  Citation: 306

  Authors: Sahel, Bahram, Jean, Meisam, Brian, Weimin

• Landsat-8 vs. Sentinel-2: examining the added value of sentinel-2’s red-edge bands to land-use and land-cover mapping in Burkina Faso

  Citation: 260

  Authors: Gerald, Kangbeni, Idriss, Jerome Ebagnerin

• Deep learning in remote sensing scene classification: a data augmentation enhanced convolutional neural network framework

  Citation: 248

  Authors: Xingrui, Xiaomin, Chunbo, Peng

• Geographic object-based image analysis (GEOBIA): emerging trends and future opportunities

  Citation: 237

  Authors: Gang, Qihao, Geoffrey J., Yinan

• Comparing fully convolutional networks, random forest, support vector machine, and patch-based deep convolutional neural networks for object-based wetland mapping using images from small unmanned aircraft system

  Citation: 230

  Authors: Tao, Amr, Jon, Victor L.

• Optimal discretization for geographical detectors-based risk assessment

  Citation: 222

  Authors: Feng, Yong, Jin-Feng

• Population Estimation Methods in GIS and Remote Sensing: A Review

  Citation: 214

  Authors: Shuo-sheng, Xiaomin, Le